analysis

Modeling Defaults of Companies in Multi-Stage SC Networks

Daniel Dumke — Mon, 28 May 2012 14:07:06 +0000

Agent-based supply chain models are build using small entities (agents), which might represent a single company.
Each of the agents has its own goals and rules of operation programmed into a computer. The interaction between several agents of this kind leads to a more realistic and complex behavior of the system.

There are several different schools for the quantitative analysis of supply chain risks. Simulation is one of them and agent-based models show several distinct advantages: They are both easier to understand and allow for a more complex system behavior, than other quantitative methods.

After this introduction I would like to have a look at a current agent-based supply chain model, which analyzes the effect of bankruptcies on supply chains. The full paper can be downloaded here.

Model

The authors focus on a comprehensive view on the supply chain: several stages (horizontally and vertically) are modeled. Figure 1 shows an exemplary supply chain.

Figure 1: Exemplary Supply Chain Structure (Mizgier et al. 2012)

Each circle represents one node or agent, the connections are drawn as lines.
The model features five additional characteristics worth mentioning:

Price dispersion (prices can vary between companies)
Evolution of supply chain topology (links between companies can be changed, by the agents themselves)
Network reconfiguration (the reconfiguration is based on the price)
Production dynamics (output is determined by the invested working capital)
Dynamics of costs of production (random changes to the environment every five periods, lead to changes in the cost function of the companies)

Last but not least, companies may go bankrupt if they are not able to perform their short therm debts. There are no loans.

Results

First, the network performs as expected. During the first period turbulences can be observed. Figure 2 shows the utilization of working capital during the simulation (1 equals 100%).

Figure 2: Capacity Utilization (Mizgier et al. 2012)

After 200 iterations a typical start scenario might look like figure 3.

Figure 3: Network Structure after Initialization Period (Mizgier et al. 2012)

Starting from this state the

The firms with the best profit/cost ratio are growing and adding new suppliers, whereas the working capital of the firms whose sales price is higher than the mean price of the given stage is slowly decaying and results in the defaults of firms.

A stable state might look like figure 4.

Figure 3: Network Structure after Stabilization (Mizgier et al. 2012)

The authors deduce three implications from those results:

The first implication is that during the process of assessment of the company’s risk exposure, managers should keep their focus on the global structure of the supply chain network instead of being restricted to the own portfolio of suppliers and customers.

Secondly, as a result of the dynamics of the topology of the supply chain network, strong competition in prices and fast changing technology, even the most reliable firms should be monitored and constantly re-evaluated in terms of their production capacity and risks associated with their structure of connections.

Third and most important, managers should find ways to cut costs and reinvest the free cash flows in new technology of production, which will allow further cost reductions and the development of new innovative and cheaper products.

Conclusion

Being stuck in the supply chain can lead to negative consequences, when ripple effects cause multiple suppliers and customers to default. Furthermore, these supply chain partners might also not be the most efficient ones, and these prices affect each participating company.
All in all, a great paper, but I would have liked to read more about the authors’ efforts to validate and verify the model’s integrity.

Reference:

Mizgier, K., Wagner, S., & Holyst, J. (2012). Modeling defaults of companies in multi-stage supply chain networks International Journal of Production Economics, 135 (1), 14-23 DOI: 10.1016/j.ijpe.2010.09.022

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Simulation

analysis

Assessment of Supply Chain Vulnerabilities

Daniel Dumke — Mon, 02 Apr 2012 14:34:31 +0000

This is a review of another chapter of the book by Zsidisin and Ritchie (Supply Chain Risk). The book can be bought at amazon.com, if you are interested in reading more.

I already reviewed chapter 15 on Behavioral Risks in Supply Networks.

The title of this weeks article is “Assessing the Vulnerability of Supply Chains” and since the author works for a consulting firm you can expect a more practice oriented approach to risk analysis.

Goals and definitions

There are three main questions to be answered in this article:

Understand the nature and types of factors that may pose threats and risks to the achievement of the supply chain system’s short and long term mission.

Understand the scenarios (processes and mechanisms) through which these threats, risks and vulnerabilities may evolve.

Understand how through the use of vulnerability scenarios, the likelihood and consequences of such threats may be reduced and managed in a cost- and service effective manner, whilst achieving an acceptable vulnerability level.

A vulnerability of a supply chain in this context is defined as

the properties of a supply chain system; its premises, facilities, and equipment, including its human resources, human organization and all its software, hardware, and net-ware, that may weaken or limit its ability to endure threats and survive accidental events that originate both within and outside the system boundaries.

Vulnerability analysis is seen as an extension to risk analysis.

Risk analysis is focused towards the human, environmental and property impacts of an accidental event, while a vulnerability analysis is focused towards the system mission and the survivability of the system.

In a risk analysis three questions make up the basis of the analysis: (i) what can go wrong, (ii) how likely is it to happen, and (iii) what are the consequences.

A vulnerability analysis, on the other hand, focuses upon (a) an extended set of threats and consequences, (b) adequate resources to mitigate and bring the system back to new stability, and © the disruption time before new stability is established [figure 1].

Figure 1: Disruption Sequence (Asbjornslett, 2009)

Vulnerability Analysis

Based on this definitions the author builds his approach on a generic approach for risk assessment. The flow chart in figure 2 highlights the seven steps.

Figure 2: Vulnerability Analysis Process (Asbjornslett, 2009)

These steps fall into three categories:

Understanding the context-specific threat and risk picture of the given supply chain and SCM context, and structure this into a taxonomy of the vulnerability factors [steps 1 to 3].

Analyse and rank the vulnerability scenarios, resulting in a criticality ranking of the scenarios [steps 4 and 5].

Handling of the vulnerability through cost- or service-effective likelihood or consequence reducing measures, bringing the vulnerability down to an acceptable level [steps 6 and 7].

The goal of the first step is to have a common understanding of the specific objectives of the vulnerability analysis, the level of analysis and setting the levels of acceptable risk.

In the second step the processes and infrastructure has to be mapped. Flows of money, information and goods are highlighted. Here, “it is recommended not to make the context description too fine-grained, but rather make notes of how the context could further be detailed if required.”

In the third step factors which lead to vulnerabilities are collected in a structured manner. Figure 3 shows a fishbone diagram with several different categories which can lead to vulnerabilities.

Figure 3: Example of Factors Contributing to Vulnerabilities (Asbjornslett, 2009)

Based on these vulnerabilities adverse scenarios are developed in the next step.

A scenario is a sequence of possible events, originating from an accidental event, where the events may be separated in time and space, and where barriers to prevent the sequence are part of the scenario.

In the next step the mentioned scenarios have to be documented. The author suggests the worksheet in figure 4.

Figure 4: Template for Scenario Documentation (Asbjornslett, 2009)

To get a better grasp of the actual criticality of the revealed vulnerabilities, each scenario has to be evaluated according to its likelihood and consequences (step 5). Figure 5 has a sample sheet.

Figure 5: Assessment of the Scenario’s Criticality (Asbjornslett, 2009)

Step 6 is about ranking the different vulnerabilities to best align risk mitigation efforts (figure 6).

Figure 6: Likelihood/Consequence Diagramm (Asbjornslett, 2009)

As such we have ‘low-criticality’ scenarios in the lower left corner (white), and ‘high-criticality’ scenarios in the upper right corner (dark grey shading). The ‘criticality areas’ should be based on the acceptance criteria developed in step one, both for the un-mitigated and the mitigated consequences.

The last step deals with finding mitigation strategies for selected scenarios. Figure 7 shows the template.

Figure 6: Mitigation Activities to Reduce Likelihood or Consequences of a Vulnerability (Asbjornslett, 2009)

Conclusion

This article presented a really business oriented approach to vulnerability analysis. In a similar manner how it could be found in a presentation of a business consultant.

This business orientation can be also seen in the structure of the article: A conclusive derivation of the process is missing completely at least the definitions are mentioned.

Nonetheless, from experience I can tell that this process contains several important steps which are also known from scientific literature. And it really is immediately applicable.

Why not include some of the insights and steps presented here in your next risk-management-meeting?

If you want to have a more scientific view on vulnerability analysis have a look at this article.

Reference:

Asbjornslet, B. E. (2009). Assessing the Vulnerability of Supply Chains Supply Chain Risk - A Handbook of Assessment, Management, and Performance, 15-33 DOI: 10.1007/978-0-387-79934-6_2

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